Recent scaling trends in semiconductor technologies including both logic and memory indicate that the current technologies will soon reach their performance limits. As a result, not only alternative technologies are being actively sought but entirely new computational frameworks are being investigated. Resistance switching devices have been investigated recently not only as a replacement for current state-of-the art flash memory technology but also for neuromorphic computation. The emerging field of neuromorphic computation which is expected to be orders of magnitude more efficient at analyzing increasingly complex exa-scale data compared to conventional Von-Neumann computation. Large volumes of data are needed to be analyzed in many different private and public sectors including social media, search engines, public health, national security and many more. Resistive switching memories are ideal candidates for creating such neuromorphic hardware due to their scalability and ease of 3D integration for achieving extremely high device density. However, resistive switching memories developed so far suffer from a variety of issues including high operating voltage and current values as well as large device-to-device and cycle-to-cycle variability. There is a need for low operating voltage and current as well as limited variability for resistive switching memory technology.